A fully convolutional network with layer-wise fine-tuning classifies malware families with over 99% accuracy across three datasets and processes obfuscated ...
A new study proposes a closed-loop AI framework that couples adaptive anomaly detection, probabilistic risk scoring, machine-learning measurement selection, game-theoretic ...
A new framework combining reinforcement learning and Siamese convolutional networks with blockchain-secured routing reports over 92 percent accuracy in detecting ...
A new analysis of Nepal's central bank and telecom data finds explosive growth in digital payments alongside persistent rural connectivity, ...
Researchers developed TriAlign-ATG, an AI framework that generates physically executable adversarial network traffic which evades deep-learning intrusion detectors while preserving ...
A cluster-preserving feature selection framework cut intrusion detection features from 78 to 27 on the CICIDS2017 benchmark while Random Forest ...
A blockchain-based framework using Ethereum smart contracts and a CNN detector blocked 99.8% of simulated DDoS attacks on client–server networks ...
A new graph neural network framework called EGNN expands rare attack samples and uses threshold-controlled pseudo-labeling to improve intrusion detection ...
A systematic review proposes a six-layer Zero Trust framework that verifies not only who controls public-safety drones but whether the ...
Researchers built a hybrid host-level dataset of 5,326 ransomware and goodware executions with 392 features, achieving F1-scores of 0.982 for ...
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